Structural atlas of human primary microRNAs generated by SHAPE-MaP.

MicroRNA (miRNA) maturation is critically dependent on structural features of primary transcripts (pri-miRNAs). However, the scarcity of determined pri-miRNA structures has limited our understanding of miRNA maturation. Here, we employed selective 2'-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP), a high-throughput RNA structure probing method, to unravel the secondary structures of 476 high-confidence human pri-miRNAs. Our SHAPE-based structures diverge substantially from those inferred solely from computation, particularly in the apical loop and basal segments, underlining the need for experimental data in RNA structure prediction. By comparing the structures with high-throughput processing data, we determined the optimal structural features of pri-miRNAs. The sequence determinants are influenced substantially by their structural contexts. Moreover, we identified an element termed the bulged GWG motif (bGWG) with a 3' bulge in the lower stem, which promotes processing. Our structure-function mapping better annotates the determinants of pri-miRNA processing and offers practical implications for designing small hairpin RNAs and predicting the impacts of miRNA mutations.

Emerging roles of RNA modification: m(6)A and U-tail.

Although more than 100 types of RNA modification have been described thus far, most of them were thought to be rare in mRNAs and in regulatory noncoding RNAs. Recent developments have unveiled that at least some of the modifications are considerably abundant and widely conserved. This Minireview summarizes the molecular machineries and biological functions of methylation (N6-methyladenosine, m(6)A) and uridylation (U-tail).

Genomic Clustering Facilitates Nuclear Processing of Suboptimal Pri-miRNA Loci.

Nuclear processing of most miRNAs is mediated by Microprocessor, comprised of RNase III enzyme Drosha and its cofactor DGCR8. Here, we uncover a hidden layer of Microprocessor regulation via studies of Dicer-independent mir-451, which is clustered with canonical mir-144. Although mir-451 is fully dependent on Drosha/DGCR8, its short stem and small terminal loop render it an intrinsically weak Microprocessor substrate. Thus, it must reside within a cluster for normal biogenesis, although the identity and orientation of its neighbor are flexible. We use DGCR8 tethering assays and operon structure-function assays to demonstrate that local recruitment and transfer of Microprocessor enhances suboptimal substrate processing. This principle applies more broadly since genomic analysis indicates suboptimal canonical miRNAs are enriched in operons, and we validate several of these experimentally. Proximity-based enhancement of suboptimal hairpin processing provides a rationale for genomic retention of certain miRNA operons and may explain preferential evolutionary emergence of miRNA operons.

TUT7 controls the fate of precursor microRNAs by using three different uridylation mechanisms.

Terminal uridylyl transferases (TUTs) function as integral regulators of microRNA (miRNA) biogenesis. Using biochemistry, single-molecule, and deep sequencing techniques, we here investigate the mechanism by which human TUT7 (also known as ZCCHC6) recognizes and uridylates precursor miRNAs (pre-miRNAs) in the absence of Lin28. We find that the overhang of a pre-miRNA is the key structural element that is recognized by TUT7 and its paralogues, TUT4 (ZCCHC11) and TUT2 (GLD2/PAPD4). For group II pre-miRNAs, which have a 1-nt 3' overhang, TUT7 restores the canonical end structure (2-nt 3' overhang) through mono-uridylation, thereby promoting miRNA biogenesis. For pre-miRNAs where the 3' end is further recessed into the stem (as in 3' trimmed pre-miRNAs), TUT7 generates an oligo-U tail that leads to degradation. In contrast to Lin28-stimulated oligo-uridylation, which is processive, a distributive mode is employed by TUT7 for both mono- and oligo-uridylation in the absence of Lin28. The overhang length dictates the frequency (but not duration) of the TUT7-RNA interaction, thus explaining how TUT7 differentiates pre-miRNA species with different overhangs. Our study reveals dual roles and mechanisms of uridylation in repair and removal of defective pre-miRNAs.

Cell adhesion-dependent control of microRNA decay.

Mammalian microRNAs (miRNAs) are highly stable in most cell types, and their decay mechanism remains largely unknown. Here we report that some miRNAs degrade rapidly upon the loss of cell adhesion. When cells are grown at low density or cells are detached by trypsinization or EGTA treatment, mature miR-141 is downregulated while miR-200c from a common primary transcript (pri-miR-200c∼141) remains unaffected. Blockade of transcription by Actinomycin D leads to rapid depletion of miR-141 with a half-life of <1 hr when cells are detached, indicating that the regulation occurs via RNA decay. A sequence motif (UGUCU) in the center of miR-141 is necessary for the regulation. We further find that many other miRNAs including miR-200a, miR-34a, miR-29b, miR-301a, and miR-21 are degraded upon cell splitting. Induced destruction of persistent regulatory molecules such as miRNAs may increase cellular plasticity and facilitate cellular remodeling in response to the changes in cell adhesion.

Re-evaluation of the roles of DROSHA, Export in 5, and DICER in microRNA biogenesis.

Biogenesis of canonical microRNAs (miRNAs) involves multiple steps: nuclear processing of primary miRNA (pri-miRNA) by DROSHA, nuclear export of precursor miRNA (pre-miRNA) by Export in 5 (XPO5), and cytoplasmic processing of pre-miRNA by DICER. To gain a deeper understanding of the contribution of each of these maturation steps, we deleted DROSHA, XPO5, and DICER in the same human cell line, and analyzed their effects on miRNA biogenesis. Canonical miRNA production was completely abolished in DROSHA-deleted cells, whereas we detected a few DROSHA-independent miRNAs including three previously unidentified noncanonical miRNAs (miR-7706, miR-3615, and miR-1254). In contrast to DROSHA knockout, many canonical miRNAs were still detected without DICER albeit at markedly reduced levels. In the absence of DICER, pre-miRNAs are loaded directly onto AGO and trimmed at the 3' end, yielding miRNAs from the 5' strand (5p miRNAs). Interestingly, in XPO5 knockout cells, most miRNAs are affected only modestly, suggesting that XPO5 is necessary but not critical for miRNA maturation. Our study demonstrates an essential role of DROSHA and an important contribution of DICER in the canonical miRNA pathway, and reveals that the function of XPO5 can be complemented by alternative mechanisms. Thus, this study allows us to understand differential contributions of key biogenesis factors, and provides with valuable resources for miRNA research.

RNATACs: Multispecific small molecules targeting RNA by induced proximity.

RNA-targeting small molecules (rSMs) have become an attractive modality to tackle traditionally undruggable proteins and expand the druggable space. Among many innovative concepts, RNA-targeting chimeras (RNATACs) represent a new class of multispecific, induced proximity small molecules that act by chemically bringing RNA targets into proximity with an endogenous RNA effector, such as a ribonuclease (RNase). Depending on the RNA effector, RNATACs can alter the stability, localization, translation, or splicing of the target RNA. Although still in its infancy, this new modality has the potential for broad applications in the future to treat diseases with high unmet need. In this review, we discuss potential advantages of RNATACs, recent progress in the field, and challenges to this cutting-edge technology.

Assessment of Lambda-Cyhalothrin and Spinetoram Toxicity and Their Effects on the Activities of Antioxidant Enzymes and Acetylcholinesterase in Honey Bee (Apis mellifera) Larvae.

Honeybees play a crucial role as agricultural pollinators and are frequently exposed to various pollutants, including pesticides. In this study, we aimed to evaluate the toxicity of lambda-cyhalothrin (LCY) and spinetoram (SPI) in honey bee larvae reared in vitro through single (acute) and repeated (chronic) exposure. The acute LD50 values for LCY and SPI were 0.058 (0.051-0.066) and 0.026 (0.01-0.045) µg a.i./larva, respectively. In chronic exposure, the LD50 values of LCY and SPI were 0.040 (0.033-0.046) and 0.017 (0.014-0.019) µg a.i./larva, respectively. The chronic no-observed-effect dose of LCY and SPI was 0.0125 µg a.i./larva. Adult deformation rates exceeded 30% in all LCY treatment groups, showing statistically significant differences compared to the solvent control group (SCG). Similarly, SPI-treated bees exhibited significantly more deformities than SCG. Furthermore, we examined the activities of several enzymes, namely, acetylcholinesterase (AChE), glutathione-S-transferase (GST), catalase (CAT), and superoxide dismutase (SOD), in larvae, pupae, and newly emerged bees after chronic exposure at the larval stage (honey bee larval chronic LD50, LD50/10 (1/10th of LD50), and LD50/20 (1/20th of LD50)). LCY and SPI induced significant changes in detoxification (GST), antioxidative (SOD and CAT), and signaling enzymes (AChE) during the developmental stages (larvae, pupae, and adults) of honey bees at sublethal and residue levels. Our results indicate that LCY and SPI may affect the development of honey bees and alter the activity of enzymes associated with oxidative stress, detoxification, and neurotransmission. These results highlight the potential risks that LCY and SPI may pose to the health and normal development of honey bees.

A Study on Survival Analysis Methods Using Neural Network to Prevent Cancers.

Background: Cancer is one of the main global health threats. Early personalized prediction of cancer incidence is crucial for the population at risk. This study introduces a novel cancer prediction model based on modern recurrent survival deep learning algorithms. Methods: The study includes 160,407 participants from the blood-based cohort of the Korea Cancer Prevention Research-II Biobank, which has been ongoing since 2004. Data linkages were designed to ensure anonymity, and data collection was carried out through nationwide medical examinations. Predictive performance on ten cancer sites, evaluated using the concordance index (c-index), was compared among nDeep and its multitask variation, Cox proportional hazard (PH) regression, DeepSurv, and DeepHit. Results: Our models consistently achieved a c-index of over 0.8 for all ten cancers, with a peak of 0.8922 for lung cancer. They outperformed Cox PH regression and other survival deep neural networks. Conclusion: This study presents a survival deep learning model that demonstrates the highest predictive performance on censored health dataset, to the best of our knowledge. In the future, we plan to investigate the causal relationship between explanatory variables and cancer to reduce cancer incidence and mortality.